IEDA2540
Statistics for Engineers
2020–2025
Course Snapshot
- Institution
- HKUST, Department of Industrial Engineering and Decision Analytics
- Period
- 2020–2025
- Audience
- Undergraduate engineering students.
- Prerequisites
- Basic calculus and algebra.
This course introduces statistical thinking for engineering systems: data description, probabilistic modeling, statistical estimation, uncertainty quantification, and evidence-based decisions.
Learning Outcomes
- Summarize and visualize engineering data effectively.
- Apply probability models to quantify uncertainty.
- Construct point estimates and confidence intervals.
- Run hypothesis tests and interpret p-values responsibly.
- Build and interpret basic linear regression models.
Lecture Modules
Materials
Course Introduction
Course goals, data-driven engineering decisions, and the role of statistics in the IEDA curriculum.
Open SlidesSampling Foundations
Population vs. sample, sampling bias, and how sampling design impacts inference quality.
Open SlidesDescriptive Statistics
Summary measures and visual diagnostics for understanding variation and central tendency.
Open SlidesProbability Distributions
Discrete and continuous distributions, random variables, and model assumptions used in practice.
Open SlidesRandom Samples
Sampling distributions, law of large numbers intuition, and central limit theorem viewpoints.
Open SlidesPoint Estimation
Estimators, bias, variance, MSE tradeoffs, and principles for good estimator construction.
Open SlidesConfidence Intervals
Interval construction, interpretation under repeated sampling, and common misuse patterns.
Open SlidesHypothesis Testing
Null/alternative setup, type I/II errors, p-values, power, and practical test selection.
Open SlidesComparing Multiple Samples
Inference with more than two groups, model assumptions, and interpretation of group differences.
Open SlidesLinear Regression
Model fitting, coefficient interpretation, diagnostics, and prediction in engineering contexts.
Open Slides